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u/Competitive_Book4151

▲ 3 r/LocalAIServers+1 crossposts

Need your help - question about agentic AI Agent OS

Hey guys,

I am building a - in my opinion - pretty advanced Agent OS right now but I am not out for ads and I hope you can help me out:

Tell me the most important things that come to your mind, if you think about agentic AI Systems - specifically about Agent OS Systems. Which capabilities should a system you would actually use have?

Are you guys even interested in local-first GDPR compliant architectures?

You would really help me by bringing your thoughts to me.

Thanks in advance!

reddit.com
u/Competitive_Book4151 — 5 days ago
▲ 0 r/softwaredevelopment

Local-first agent framework I've been building solo. Default backend is Ollama (qwen3 models), with vLLM/OpenAI/Anthropic/Gemini + 14 others as opt-in. Runs on Windows, Linux, Mac, plus Flutter Command Center for Android/iOS/Web.

What's in it:

\- 127+ MCP tools across 30+ modules (memory, kanban, web research, file ops, code execution)

\- 16 channels — CLI, Telegram, Discord, Slack, WhatsApp, Voice, WebUI, more

\- PGE-Trinity orchestration (Planner / Gatekeeper / Executor) with risk classification on every tool call

\- Pack-plugin system with EULA click-through + SHA-256 hash pinning

\- 6-tier memory + audit-chain JSONL + Trace-UI (live WebSocket view of the agent loop)

The unusual part:

\- \~14 500 tests, 89% coverage gate, mypy --strict, spec-first development

\- Heuristic constants live in YAML, not source

\- Currently shipping a neuro-symbolic program synthesis engine over ARC-AGI-3: Phase 1 done with measured uplift, Phase 2 (LLM-prior + MCTS + 3-zone refiner) landing this sprint

pip install cognithor — Apache 2.0, code at github.com/Alex8791-cyber/cognithor

Curious what you'd want it to do that it can't yet.

reddit.com
u/Competitive_Book4151 — 13 days ago
▲ 6 r/CognithorAgentOS

Local-first agent framework I've been building solo. Default backend is Ollama (qwen3 models), with vLLM/OpenAI/Anthropic/Gemini + 14 others as opt-in. Runs on Windows, Linux, Mac, plus Flutter Command Center for Android/iOS/Web.

What's in it:

- 127+ MCP tools across 30+ modules (memory, kanban, web research, file ops, code execution)

- 16 channels — CLI, Telegram, Discord, Slack, WhatsApp, Voice, WebUI, more

- PGE-Trinity orchestration (Planner / Gatekeeper / Executor) with risk classification on every tool call

- Pack-plugin system with EULA click-through + SHA-256 hash pinning

- 6-tier memory + audit-chain JSONL + Trace-UI (live WebSocket view of the agent loop)

The unusual part:

- ~14 500 tests, 89% coverage gate, mypy --strict, spec-first development

- Heuristic constants live in YAML, not source

- Currently shipping a neuro-symbolic program synthesis engine over ARC-AGI-3: Phase 1 done with measured uplift, Phase 2 (LLM-prior + MCTS + 3-zone refiner) landing this sprint

pip install cognithor — Apache 2.0, code at github.com/Alex8791-cyber/cognithor

Curious what you'd want it to do that it can't yet.

reddit.com
u/Competitive_Book4151 — 13 days ago
▲ 22 r/CognithorAgentOS

We've been working on this project for about a year and just pushed v0.86.0. Cognithor is a full Agent Operating System, not just another "LLM with a few tools" wrapper. Figured I'd share since the project has grown to a point where it's actually worth comparing to other frameworks.

The core is a PGE loop: Planner, Gatekeeper, Executor, where the Gatekeeper evaluates risk at four levels before any tool gets called. Tools run in DAG-parallel so multi-step tasks don't block each other. Memory is split across six cognitive tiers (Core, Episodic, Semantic, Procedural, Working, Tactical) and searched via a fusion of BM25, vector similarity and graph traversal with recency decay.

On the tooling side there are 125+ MCP tools covering filesystem, shell, web, Playwright browser automation, Computer Use and a Reddit Lead Hunter that handles the full scan → score → draft → reply → learn cycle autonomously. Seventeen LLM providers are supported including full offline operation via Ollama, LM Studio, vLLM and llama.cpp, with a task-aware model router that assigns different models to planning, execution and coding tasks.

GDPR compliance is built into the message pipeline, not bolted on: every message passes through a compliance gate before anything else happens, and the audit trail uses a SHA-256 hash chain with Ed25519 signatures. All SQLite databases are encrypted with SQLCipher AES-256 and keys live in the OS keyring.

The test suite is at 13,117 tests with 89% coverage, zero lint errors and a green CI. I attached a three-image comparison against Hermes and OpenClaw so you can see exactly where things stand.

Apache 2.0 · Python 3.12+

ghttps://github.com/Alex8791-cyber/cognithor

Happy to answer anything about the architecture, the GDPR layer or local LLM setup.

u/Competitive_Book4151 — 1 month ago